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Find an admissible ensemble assignment B Color GB using some heuristic graph coloring procedure For the second stage, a plethora of different graph coloring algorithms is already available In fact, it turns out that fairly simple sequential coloring methods like Br laz DSATUR [2] or Matula/Beck s smallest-last algorithm [16] usually perform very well on the kind of geometric graphs arising as models of broadcast networks So we can concentrate on the first step, which is a kind of simultaneous bin packing problem The difficult part here is to devise a packing strategy that reduces the global number of required colors instead of merely optimizing the packings for individual areas Of course, no single strategy will work with all area graphs equally well, so let us take a look at the two extreme cases: Independent (edgeless) graphs.

In this case the problem decomposes into |V | indeR pendent bin packing problems, one for each vertex of G, and we have that M (G) = max{pM(Rv) : v V} Complete graphs In this case, all distinct ensembles will have to be assigned differR ent frequency blocks, hence M (G) = pM(RV) (Here and in the following, by RW we denote the set of all requested services in a set of areas W V, ie, RW = w WRw Thus, in particular, RV is the set of all requested services) This suggests two different types of ensemble packing heuristics that we would expect to work reasonably well with sparse and dense graphs, respectively For sparse graphs, we will pack the individual requirement sets independently of each other.

For dense graphs, we will pack the entire collection of requested services, and then assign to each vertex those ensembles that are needed to satisfy the supply requirements at that vertex For an arbitrary graph, we might try both approaches and see which one works best Since the bin packing problem is NP-complete, we need some heuristic for solving it A simple method, which works reasonably well, is the so-called first-fit (FF) algorithm, which considers the items (aka services) to be packed in some order, and puts each item into the first bin (aka ensemble) into which it fits It is well-known that if the items are ordered by decreasing sizes, this method never performs worse than 11/9 times the optimum (asymptotically) (We refer the reader to [7] for the details of this algorithm and its analysis.

) In the following, we use FF(R, M) to denote the set of ensembles returned by the firstfit algorithm when applied to a set of required services R S and the ensemble size M We can employ the first-fit algorithm for packing ensembles using the two strategies sketched out above, as follows: Simultaneous First-Fit (SFF) Algorithm: Bv = FF(Rv, M) v V (125).

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Experiences that challenge, threaten, or invalidate core assumptions or general beliefs about the world serve as the potential foundations for dysphoria and/or maladaptive coping patterns. Traumatic events serve to challenge, threaten, or invalidate core characterologically based assumptions, hence their pathogenic nature. Continuing this theme of pivotal assumptions, Everly (1993a, 1993b, 1994) noted that human beings require and, therefore, create, explanatory worldviews concerning themselves and their respective environments. Everly (1993, 1994, 1995), borrowing from the field of rhetoric, has referred to these deeply held belief systems as Weltanschauung (from the German welt, meaning world and anschauung, meaning perspective ). These beliefs serve as important assumptive models, or explanatory worldviews, that bring order to what may be an otherwise chaotic environment. These worldviews, or Weltanschauung, as described by Everly (1995), serve not only as explanatory constructs with anxiolytic properties, but at the same time, potential diatheses, or psychological vulnerabilities, if threatened or destroyed. Based on a review of relevant literature, there appear to be at least five core psychological themes that have relevance in understanding psychological trauma: 1. 2. 3. 4. 5. The need for attachment to, and trust in, others. The need for a positive self-identity, view of self, that is, self-esteem, self-efficacy. The belief in a fair and just world. The need for physical safety. The belief in some overarching order to life, for example, spirituality, or faith in a defining order, unifying paradigm, and so on.

Although this is the last chapter on a topic directly relevant to best practices for dashboard deployment (the following chapters explore various case studies), the content herein actually deals with the first step in the due diligence process for a dashboard deployment. Once there is an organizational mandate to deploy a dashboard for a certain initiative, such as Balanced Scorecard, enterprise performance management, supply chain, customer service, and so on, four immediate issues must be examined:

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